Promoting early childhood oral health and preventing early childhood caries on Instagram
Bibliographic record
Abstract
Introduction: Early childhood caries (ECC) is prevalent worldwide. Oral health promotion effectively utilizes key messages to educate parents/caregivers and the public on how to prevent ECC. Instagram is one of the biggest social media platforms, and could be used to promote early childhood oral health. The purpose of this study was to determine if and how young children's oral health is promoted and supported on Instagram. Methods: This study used inductive content analysis to categorize, quantify, and interpret pictorial and textual data derived from Instagram posts containing the most commonly used ECC-related hashtags in their captions (determined by an extensive search through Instagram's search bar). Results: A total of 1,071 images and 3,228 comments were analyzed based on 13 hashtags. The most common types of images were those of people (57.5%) and graphics/memes (37.8%). Most people were older children (32.5%) or adults (20.3%), and were White (19.6%) or Asian (18.5%). A majority of images had people posing (79.1%) in dental clinics (81.3%). Most graphics/memes were instructional/informational (76.3%). A total of 173 posts had substantial discussions that were positive/constructive in nature. The majority of discussions had at least one comment providing advice, tips, or explanations (79.8%), or had users requesting further information (73.4%). Conclusion: As more people engage with social media, health professionals should consider the potential for Instagram as a tool to promote early childhood oral health and to prevent ECC. Our study shows that many different users are providing and consuming content related to ECC. Targeted messaging, monitoring of content, and professional guidance could be beneficial to those seeking oral health information on this platform.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".